Combined Heat and Power Dynamic Economic Dispatch incorporating pumped hydro energy storage considering uncertainty and outage of renewable energy sources

Owing to increasing penetration of renewable energy sources, it is mandatory to investigate it’s effect on the combined heat and power dynamic economic dispatch. At the same time , adverse effect is there due to highly intermittent nature and higher rate of outages of these sources . This piece of work proposes squirrel search algorithm (SSA) for solving combined heat and power dynamic economic dispatch (CHPDED) incorporating pumped-storage-hydraulic unit captivating uncertainty and outage of renewable energy sources. A lately developed swarm intelligence algorithm SSA, emulates from the dynamic scavenging behavior of squirrel. The competence of the recommended technique is examined on a test system. Simulation outcomes of the proposed technique is harmonized with those acquired by particle swarm optimization (PSO) and grey wolf optimization (GWO). After comparison, a conclusion was made presenting SSA technique conferring with good-quality solution than other techniques.

Some Properties for Adjustable Entropy Function Method and Its Application to Power Systems Dynamic Economic Dispatch with Wind Integrated

With the continuous development of the world economy, the development and utilization of environmentally friendly and renewable energy have become the trend in many countries. In this paper, we study the dynamic economic dispatch with wind integrated. Firstly, we take advantage of the positive and negative spinning reserve to deal with wind power output prediction errors in order to establish a dynamic economic dispatch model of wind integrated. The existence of a min function makes the dynamic economic dispatch model nondifferentiable, which results in the inability to directly use the traditional mathematical methods based on gradient information to solve the model. Inspired by the aggregate function, we can easily transform the nondifferentiable model into a smooth model when parameter p tends to infinity. However, the aggregate function will cause data overflow when p tends to infinity. Then, for solving this problem, we take advantage of the adjustable entropy function method to replace of aggregate function method. In addition, we further discuss the adjustable entropy function method and point out that the solution generated by the adjustable entropy function method can effectively approximate the solution of the original problem without parameter p tending to infinity. Finally, simulation experiments are given, and the simulation results prove the effectiveness and correctness of the adjustable entropy function method.